






'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					   =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					   =				{
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}




class 				lowerCAmelCase       (				_A ):







   """simple docstring"""







   _A							= """data2vec-vision"""



   def __init__(			self , _A=768 , _A=12 , _A=12 , _A=3072 , _A="gelu" , _A=0.0 , _A=0.0 , _A=0.02 , _A=1E-1_2 , _A=224 , _A=16 , _A=3 , _A=False , _A=False , _A=False , _A=False , _A=0.1 , _A=0.1 , _A=True , _A=[3, 5, 7, 11] , _A=[1, 2, 3, 6] , _A=True , _A=0.4 , _A=256 , _A=1 , _A=False , _A=255 , **_A , )     -> List[str]:
    super().__init__(**snake_case_      )

    __a     :						Optional[int]          =    hidden_size
    __a     :						Union[str, Any]          =    num_hidden_layers
    __a     :						Dict          =    num_attention_heads
    __a     :						List[Any]          =    intermediate_size
    __a     :						Dict          =    hidden_act
    __a     :						Union[str, Any]          =    hidden_dropout_prob
    __a     :						Optional[Any]          =    attention_probs_dropout_prob
    __a     :						str          =    initializer_range
    __a     :						Dict          =    layer_norm_eps

    __a     :						Optional[int]          =    image_size
    __a     :						List[str]          =    patch_size
    __a     :						Union[str, Any]          =    num_channels
    __a     :						Any          =    use_mask_token
    __a     :						Optional[int]          =    use_absolute_position_embeddings
    __a     :						List[Any]          =    use_relative_position_bias
    __a     :						Union[str, Any]          =    use_shared_relative_position_bias
    __a     :						Tuple          =    layer_scale_init_value
    __a     :						Union[str, Any]          =    drop_path_rate
    __a     :						Optional[Any]          =    use_mean_pooling
    # decode head attributes (semantic segmentation)
    __a     :						str          =    out_indices
    __a     :						Any          =    pool_scales
    # auxiliary head attributes (semantic segmentation)
    __a     :						Optional[int]          =    use_auxiliary_head
    __a     :						List[str]          =    auxiliary_loss_weight
    __a     :						Optional[Any]          =    auxiliary_channels
    __a     :						Any          =    auxiliary_num_convs
    __a     :						List[str]          =    auxiliary_concat_input
    __a     :						List[str]          =    semantic_loss_ignore_index




class 				lowerCAmelCase       (				_A ):







   """simple docstring"""







   _A							= version.parse('1.11' )



   @property
   def  __magic_name__     (			self      )     -> List[Any]:
    return OrderedDict(
        [
            ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
        ]      )



   @property
   def  __magic_name__     (			self      )     -> str:
    return 1E-4






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch

							from transformers.generation import DisjunctiveConstraint

@require_torch
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							Dict   ):
							# For consistency across different places the DisjunctiveConstraint is called,
							# dc.token_ids is a list of integers. It is also initialized only by integers.

							UpperCamelCase_:				Optional[int]						=    [[1, 2, 4], [1, 2, 3, 4]]
							UpperCamelCase_:				List[str]						=    DisjunctiveConstraint(snake_case_   )
							self.assertTrue(isinstance(dc.token_ids    ,						snake_case_   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							# We can't have constraints that are complete subsets of another. This leads to a preverse
							# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
							# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
							# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
							# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
							UpperCamelCase_:				Dict						=    [[1, 2], [1, 2, 3, 4]]

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(snake_case_   )  # fails here

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Dict						=    [[1, 2, 3], [1, 2, 4]]

							UpperCamelCase_:				Dict						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    dc.update(1   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(2   )
							UpperCamelCase_:				Tuple						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(3   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is True and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 3]   )




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

							UpperCamelCase_:				Tuple						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Dict						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Any						=    dc.update(4   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2, 4]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

							dc.reset()

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 3   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Tuple						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 2   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[str]						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.remaining() == 0   )
							self.assertTrue(dc.current_seq == [1, 2, 5]   )






